SoroushVahidi/scidocs huggingface dataset:
A Multiphase Switched-Capacitor DC–DC Converter Ring With Fast Transient Response and Small Ripple
(2017)

A fully integrated step-down switched-capacitor dc–dc converter ring with 123 phases has been designed that could achieve fast dynamic voltage scaling for the microprocessor of wearable devices. The symmetrical multiphase converter ring surrounds its load in the square and supplies power to the on-chip power grid that is easily accessible at any point of the chip edges. The frequency of the <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {DD}}$ </tex-math></inline-formula>-controlled oscillator is adjusted through its supply voltage <inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {DD}}$ </tex-math></inline-formula>, which allows the unity-gain frequency to be designed higher than the switching frequency. The converter ring has been fabricated in a low-leakage 65-nm CMOS process. This converter achieves a response time of 3 ns, a reference tracking speed of 2.5 V/<inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula>, and a minimum output ripple of 2.2 mV. The peak efficiency is 80% at the power density of 66.6 mW/mm<sup>2</sup>, and the maximum power density is 180 mW/mm<sup>2</sup>.
U-Air: when urban air quality inference meets big data
(2013)

Information about urban air quality, e.g., the concentration of PM2.5, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this paper, we infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location. We evaluated our approach with extensive experiments based on five real data sources obtained in Beijing and Shanghai. The results show the advantages of our method over four categories of baselines, including linear/Gaussian interpolations, classical dispersion models, well-known classification models like decision tree and CRF, and ANN.
Key Estimation in Electronic Dance Music
(2016)

In this paper we study key estimation in electronic dance music, an umbrella term referring to a variety of electronic music subgenres intended for dancing at nightclubs and raves. We start by defining notions of tonality and key before outlining the basic architecture of a template-based key estimation method. Then, we report on the tonal characteristics of electronic dance music, in order to infer possible modifications of the method described. We create new key profiles combining these observations with corpus analysis, and add two pre-processing stages to the basic algorithm. We conclude by comparing our profiles to existing ones, and testing our modifications on independent datasets of pop and electronic dance music, observing interesting improvements in the performance or our algorithms, and suggesting paths for future research.
Weed Detection Using Fractal-Based Low Cost Commodity Hardware Raspberry Pi
(2016)

Conventional weed control system is usually used by spraying herbicides uniformly throughout the land. Excessive use of herbicides on an ongoing basis can produce chemical waste that is harmful to plants and soil. The application of precision agriculture farming in the detection process in order to control weeds using Computer Vision On Farm becomes interesting, but it still has some problems due to computer size and power consumption. Raspberry Pi is one of the minicomputer with low price and low power consumption. Having computing like a desktop computer with the open source Linux operating system can be used for image processing and weed fractal dimension processing using OpenCV library and C programming. This research results the best fractal computation time when performing the image with dimension size of 128 x 128 pixels. It is about 7 milliseconds. Furthermore, the average speed ratio between personal computer and Raspberry Pi is 0.04 times faster. The use of Raspberry Pi is cost and power consumption efficient compared to personal computer.
Cryptocash, cryptocurrencies, and cryptocontracts
(2016)

One of the central challenges for mathematical cryptography is to create a payment system that provides the advantages of cash in a digital world. In this expository article we describe two very different solutions to this problem. The first is an elliptic-curve-based version of a construction of S. Brands, and the second is Bitcoin. We also discuss a generalization of Bitcoin that supports peer-to-peer contracts. AMS subject classifications. 94A60, 68P25, 14G50, 94-02.
Greenhouse Temperature and Humidity Intelligent Control System
(2009)

The article based on embedded database of greenhouse temperature and humidity control system intelligent. Put forward by embedded database system set up in an ideal environment for data greenhouse temperature and humidity control, greenhouse crops in the process of growth under control. To address the growth of greenhouse crops in the process of temperature and humidity controlled environment is not ideal, at the same time improve the efficiency of control and cost-effective system. The article focuses on the structure of the control system, hardware, software design and system control strategy. The control system has a simple hardware structure, cost-effective, easy to use and maintenance, temperature and humidity data, and other advantages of good stability. Key-Words: Embedded database, Temperature and Humidity, Control ; data filtering, Greenhouse,
Modular switched capacitor voltage multiplier topology for pulsed power supply
(2014)

Along with rapid advancement of power semiconductors, voltage multipliers have introduced new series of pulsed power generators. In this paper, based on conventional voltage multiplier and by using power electronics switches a new topology in high voltage pulsed power application is proposed. This topology is a modular circuit that can generate a high output voltage from a relatively low input voltage with fast rise time and adjustable frequency, pulse width, and voltage levels using a series connection of switched capacitor cells. A comparative analysis is carried out to show the advantages of proposed topology. Experimental and simulation results are presented to confirm the analysis.
Effect of transdermal teriparatide administration on bone mineral density in postmenopausal women.
(2010)

CONTEXT
Treatment of osteoporosis with an anabolic agent, teriparatide [human PTH 1-34 (TPTD)], is effective in reducing incident fractures, but patient resistance to daily sc injections has limited its use. A novel transdermal patch, providing a rapid, pulse delivery of TPTD, may provide a desirable alternative.


OBJECTIVE
The aim of the study was to determine the safety and efficacy of a novel transdermal TPTD patch compared to placebo patch and sc TPTD 20-microg injection in postmenopausal women with osteoporosis.


DESIGN
Our study consisted of 6-month, randomized, placebo-controlled, positive control, multidose daily administration.


PATIENTS
We enrolled 165 postmenopausal women (mean age, 64 yr) with osteoporosis.


INTERVENTIONS
A TPTD patch with a 20-, 30-, or 40-microg dose or a placebo patch was self-administered daily for 30-min wear time, or 20 microg of TPTD was injected daily.


OUTCOMES
The primary efficacy measure was mean percentage change in lumbar spine bone mineral density (BMD) from baseline at 6 months.


RESULTS
TPTD delivered by transdermal patch significantly increased lumbar spine BMD vs. placebo patch in a dose-dependent manner at 6 months (P < 0.001). TPTD 40-microg patch increased total hip BMD compared to both placebo patch and TPTD injection (P < 0.05). Bone turnover markers (procollagen type I N-terminal propeptide and C-terminal cross-linked telopeptide of type I collagen) increased from baseline in a dose-dependent manner in all treatment groups and were all significantly different from placebo patch (P < 0.001). All treatments were well tolerated, and no prolonged hypercalcemia was observed.


CONCLUSION
Transdermal patch delivery of TPTD in postmenopausal women with osteoporosis for 6 months is safe and effective in increasing lumbar spine and total hip BMD.
Modeling and analysis of electric field and electrostatic adhesion force generated by interdigital electrodes for wall climbing robots
(2013)

A model is presented for the analysis of the electric field and electrostatic adhesion force produced by interdigital electrodes. Assuming that the potential varies linearly with distance in inter-electrode gaps, the potential distribution on the electrode plane is obtained by taking the first-order Taylor series approximation. The expressions of electric field components are then derived by solving the Laplace equation for the electrical potential in each subregion. The electrostatic adhesion force is calculated using the Maxwell stress tensor formulation. The dynamic properties of the electric field and electrostatic adhesion force are assessed by evaluating the transient response of the field and force under a step in applied voltages. To verify the model developed, an experimental study is carried out in conjunction with the theoretical analysis to evaluate the adhesion performance of an electrode panel on a glass pane. A double tracked wall climbing robot is designed and tested on various wall surfaces. The limit of the approximation method of the inter-electrode potential is discussed. It is found that vacuum suction force is involved in the adhesion. The influence of this vacuum suction force on electrostatic adhesion is also discussed. The results of this work would provide support for theoretical guidelines and system optimization for the electrostatic adhesion technology applied to wall climbing robots.
Investigating the usability of iPad mobile tablet in formative assessment of a mathematics course
(2012)

Formative assessment allows students to judge their learning progress against a given standard or assessment criteria during the course of study. There are challenges related to the generation and delivery of feedback for formative purposes, for instance the students' engagement in their learning, the pedagogy adopted and the usability of the technology tools used in this regard. This paper presents a study of iPad mobile tablet technology-supported peer-to-peer assessment (P2PASS), which is a modern form of formative assessment. The experimental work was undertaken at Kigali Institute of Science and Technology (KIST) in Rwanda. The implementation of P2PASS in this study shows the students' engagement in learning mathematics, and allowed the investigation on the possibilities of improving their mastery of the subject. This paper further discusses the students' attitudes towards the P2PASS process as well as the technological tools used. The usability problems of the iPad applications are discussed, together with the implications of using mobile tablet technology in providing the feedback on the mathematical work.
Analysis, Design and Realization of a Novel Directive Ultrawideband Antenna
(2009)

In this paper, we present a simple log-periodic-dipole-array (LPDA) solution that allows us to achieve good ultrawideband (UWB) performances. The antenna has been manufactured and the measurements agree well with the theoretical predictions. The antenna presents an average gain of 8 dB and a return loss better than -10 dB over the band from 4.2 to 10.6 GHz. Both the measured antenna transfer function and the computed effect on pulse transmission show good performances in comparison with already known UWB antennas.
Power Budgets for CubeSat Radios to Support Ground Communications and Inter-Satellite Links
(2017)

CubeSats are a class of pico-satellites that have emerged over the past decade as a cost-effective alternative to the traditional large satellites to provide space experimentation capabilities to universities and other types of small enterprises, which otherwise would be unable to carry them out due to cost constraints. An important consideration when planning CubeSat missions is the power budget required by the radio communication subsystem, which enables a CubeSat to exchange information with ground stations and/or other CubeSats in orbit. The power that a CubeSat can dedicate to the communication subsystem is limited by the hard constraints on the total power available, which are due to its small size and light weight that limit the dimensions of the CubeSat power supply elements (batteries and solar panels). To date, no formal studies of the communications power budget for CubeSats are available in the literature, and this paper presents a detailed power budget analysis that includes communications with ground stations as well as with other CubeSats. For ground station communications, we outline how the orbital parameters of the CubeSat trajectory determine the distance of the ground station link and present power budgets for both uplink and downlink that include achievable data rates and link margins. For inter-satellite communications, we study how the slant range determines power requirements and affects the achievable data rates and link margins.
DroidLink: Automated Generation of Deep Links for Android Apps
(2016)

The mobile application (app) has become the main entrance to access the Internet on handheld devices. Unlike the Web where each webpage has a global URL to reach directly, a specific “content page” of an app can be opened only by exploring the app with several operations from the landing page. The interoperability between apps is quite fixed and thus limits the value-added “linked data” between apps. Recently, deep link has been proposed to enable targeting and opening a specific page of an app externally with an accessible uniform resource identifier (URI). However, implementing deep link for mobile apps requires a lot of manual efforts by app developers, which can be very error-prone and time-consuming. In this paper, we propose DroidLink to automatically generating deep links for existing Android apps. We design a deep link model suitable for automatic generation. Then we explore the transition of pages and build a navigation graph based on static and dynamic analysis of Android apps. Next, we realize an updating mechanism that keeps on revisiting the target app and discover new pages, and thus generates deep links for every single page of the app. Finally, we repackage the app with deep link supports, but requires no additional deployment requirements. We generate deep links for some popular apps and demonstrate the feasibility of DroidLink.
Experiments With Some Programs That Search Game Trees
(1969)

Many problems in artificial intelligence involve the searching of large trees of alternative possibilities—for example, game-playing and theorem-proving. The problem of efficiently searching large trees is discussed. A new method called “dynamic ordering” is described, and the older minimax and Alpha-Beta procedures are described for comparison purposes. Performance figures are given for six variations of the game of kalah. A quantity called “depth ratio” is derived which is a measure of the efficiency of a search procedure. A theoretical limit of efficiency is calculated and it is shown experimentally that the dynamic ordering procedure approaches that limit.
Learning Continuous Control Policies by Stochastic Value Gradients
(2015)

We present a unified framework for learning continuous control policies using backpropagation. It supports stochastic control by treating stochasticity in the Bellman equation as a deterministic function of exogenous noise. The product is a spectrum of general policy gradient algorithms that range from model-free methods with value functions to model-based methods without value functions. We use learned models but only require observations from the environment instead of observations from model-predicted trajectories, minimizing the impact of compounded model errors. We apply these algorithms first to a toy stochastic control problem and then to several physics-based control problems in simulation. One of these variants, SVG(1), shows the effectiveness of learning models, value functions, and policies simultaneously in continuous domains.
MedScan, a natural language processing engine for MEDLINE abstracts
(2003)

MOTIVATION
The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications.


RESULTS
We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.
Integrated 105 dB SNR, 0.0031% THD+N Class-D Audio Amplifier With Global Feedback and Digital Control in 55 nm CMOS
(2015)

It is traditionally challenging to implement higher-order PWM closed-loop Class-D audio amplifiers using analog intensive techniques in deep-submicron, low voltage process technologies. This is primarily attributed to reduced power supply, degraded analog transistor characteristics, including short-channel effects, increased flicker noise, random telegraph noise, transistor reliability concerns and passive component performance. In this paper, we introduce a global closed-loop mixed-signal architecture incorporating digital control and integrate a fourth-order amplifier prototype in 55 nm CMOS. A systematic approach to analyze, design and compensate the feedback loop in the digital domain is also presented. The versatility of implementing the loop gain poles and zeros digitally attains high gain throughout the audio band and attenuates residual high frequency ripples around the loop, simultaneously accomplishing improvements in THD+N and PSRR. The overall architecture is inherently amenable to implementation in deep-submicron and is therefore compatible with scaled CMOS. The measured prototype achieves a high 105 dBA SNR, 0.0031% THD+N, 92 dB PSRR and 85% efficiency when supplying 1 W into emulated 8 Ω speaker load. This performance is competitive with conventional designs using large feature size precision CMOS or specialized BCD technologies and reports the highest output power (1.5 W) for deep-submicron designs.
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
(2018)

Liver cancer is one of the leading causes of cancer death. To assist doctors in hepatocellular carcinoma diagnosis and treatment planning, an accurate and automatic liver and tumor segmentation method is highly demanded in the clinical practice. Recently, fully convolutional neural networks (FCNs), including 2-D and 3-D FCNs, serve as the backbone in many volumetric image segmentation. However, 2-D convolutions cannot fully leverage the spatial information along the third dimension while 3-D convolutions suffer from high computational cost and GPU memory consumption. To address these issues, we propose a novel hybrid densely connected UNet (H-DenseUNet), which consists of a 2-D DenseUNet for efficiently extracting intra-slice features and a 3-D counterpart for hierarchically aggregating volumetric contexts under the spirit of the auto-context algorithm for liver and tumor segmentation. We formulate the learning process of the H-DenseUNet in an end-to-end manner, where the intra-slice representations and inter-slice features can be jointly optimized through a hybrid feature fusion layer. We extensively evaluated our method on the data set of the MICCAI 2017 Liver Tumor Segmentation Challenge and 3DIRCADb data set. Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
Multi-layer dependability: From microarchitecture to application level
(2014)

We show in this paper that multi-layer dependability is an indispensable way to cope with the increasing amount of technology-induced dependability problems that threaten to proceed further scaling. We introduce the definition of multi-layer dependability and present our design flow within this paradigm that seamlessly integrates techniques starting at circuit layer all the way up to application layer and thereby accounting for ASIC-based architectures as well as for reconfigurable-based architectures. At the end, we give evidence that the paradigm of multi-layer dependability bears a large potential for significantly increasing dependability at reasonable effort.
Predicting MOOC Dropout over Weeks Using Machine Learning Methods
(2014)

With high dropout rates as observed in many current larger-scale online courses, mechanisms that are able to predict student dropout become increasingly important. While this problem is partially solved for students that are active in online forums, this is not yet the case for the more general student population. In this paper, we present an approach that works on click-stream data. Among other features, the machine learning algorithm takes the weekly history of student data into account and thus is able to notice changes in student behavior over time. In the later phases of a course (i.e., once such history data is available), this approach is able to predict dropout significantly better than baseline methods.
Predicting good probabilities with supervised learning
(2005)

We examine the relationship between the predictions made by different learning algorithms and true posterior probabilities. We show that maximum margin methods such as boosted trees and boosted stumps push probability mass away from 0 and 1 yielding a characteristic sigmoid shaped distortion in the predicted probabilities. Models such as Naive Bayes, which make unrealistic independence assumptions, push probabilities toward 0 and 1. Other models such as neural nets and bagged trees do not have these biases and predict well calibrated probabilities. We experiment with two ways of correcting the biased probabilities predicted by some learning methods: Platt Scaling and Isotonic Regression. We qualitatively examine what kinds of distortions these calibration methods are suitable for and quantitatively examine how much data they need to be effective. The empirical results show that after calibration boosted trees, random forests, and SVMs predict the best probabilities.